University of Heidelberg
Faculty of Medicine Mannheim
University Hospital Mannheim
News
CKM receives DFG funding for Project "Prdiktion von Therapieansprechen und Outcome beim lokal fortgeschrittenen Rektum-Karzinom mittels Radiomics und Deep Learning: eine beispielhafte Anwendung fr eine allgemein verwendbare, Deep Learning basierte Prozessierungs-Pipeline fr die Bild-Klassifikation" read more.
Alena-Kathrin Schnurr presented on our research on AI in medical imaging @ Research Plus forum
read more
Dr. Mathias Davids wins the I. I. Rabi Award of the International Society for Magnetic Resonance in Medicine (ISMRM),
read more, article in local newspaper (Mannheimer Morgen)
Note


If you have questions concerning a specific publication please use this form with subject 'information about publications' and giving the full citation in the message body.

Links
Collaborate Research Projects
Home > Publications > Abstract >

A fully automated method for predicting glioma patient outcome from DSC imaging. A second reference to histopathology?

K. Emblem, F. Zöllner and A. Bjornerud

Proceedings 17th Scientific Meeting, International Society for Magnetic Resonance in Medicine, 17, p.281

We have assessed whether a fully automated, multi-parametric model for predicting outcome in glioma patients from dynamic susceptibility contrast MR imaging can be used as a second reference to pathologic findings. Based on automatically segmented tumor regions, 3D scatter diagrams of cerebral blood volume as a function of Ktrans were derived for each patient. A predictive model based on support vector machines was used to predict outcome in each patient using scatter diagrams and survival status of the remaining patients. Our results suggest that the proposed approach provides similar diagnostic accuracy values to histopathology when predicting patient outcome.

Contact: Prof. Dr. Frank Zöllner last modified: 30.09.2020
to top of page